Pathogenic classification
We applied the American College of Medical Genetics and Genomics (ACMG) and the Association for Molecular Pathology (AMP) guidelines (Richards et al., 2015) in our study. We assessed the predicted pathogenicity and benign impact of the variants found in our cohort. To do so, we revised the ACMG/AMP rules to determine which criteria apply to our framework for GGE-related EFHC1 variants.
We performed a series of analyses for each identified EFHC1variant. To address whether the identified amino acid changes were already established as pathogenic or benign, we performed a literature search using the online search engine PubMed (https://www.ncbi.nlm.nih.gov/pubmed/) for the terms ´EFHC1 AND (mutation OR variants) AND epilepsy´ up to April 2019. We further assessed the frequency of the variants found in EFHC1 in databases of individuals from different populations: BIPMed (http://bipmed.org/; Secolin et al., 2019), ABraOM (http://abraom.ib.usp.br/; Naslavsky et al., 2017), NHLBI Exome Sequencing Project (ESP) (http://evs.gs.washington.edu/EVS/), gnomAD (http://gnomad.broadinstitute.org/), and 1000 Genomes (http://www.internationalgenome.org/). We also investigated theEFHC1 variants found in our cohort in 100 unrelated Brazilian individuals without a family history of epilepsy. To verify whether the prevalence of these variants in affected individuals was statistically increased over controls, we performed the unconditional exact homogeneity/independence test (Z-pooled method, one-tailed). If the odds ratio (OR) calculated is 1.00, then there is no association between the variant and the risk for the disease. On the other hand, values greater than 1.00 indicate that the variant increased the odds of having the disease (Bailey et al., 2017). To avoid the possibility of population stratification, we performed separate tests for each of the control groups that contained admixed Brazilian individuals (our 100 individuals control group, as well as the BIPMed and AbraOM databases).
In order to predict the deleterious effect of EFHC1 missense variants in protein function, we used 13 of the 16 computer algorithms recommended by the ACMG/AMP guidelines: FATHMM (http://fathmm.biocompute.org.uk; Shihab et al., 2015), Condel (http://bg.upf.edu/condel; González-Pérez & Lopez-Bigas, 2011), MutationTaster (http://www.mutationtaster.org; Schwarz, Rodelsperger, Schuelke, & Seelow, 2010), PANTHER (http://www.pantherdb.org/tools; Mi, Muruganuian, & Thomas, 2013), SNPs&GO (http://snps.biofold.org/snps-and-go/snps-and-go.html; Calabrese Capriotti, Fariselli, Martelli, & Casadio, 2009), MutPred2 (http://mutpred.mutdb.org; Pejaver et al., 2017), PROVEAN (http://provean.jcvi.org; Choi, Sims, Murphy, Miller, & Chan, 2012), CADD (http://cadd.gs.washington.edu; Rentzsch, Witten, Cooper, Shendure, & Kircher, 2019), PolyPhen2 (http://genetics.bwh.harvard.edu/pph2; Adzhubei, Jordan, & Suyaev, 2013), MutationAssessor (http://mutationassessor.org/r3; Reva, Antipin, & Sander, 2011), SIFT (http://siftdna.org; Sim et al., 2012), Align GVGD (http://agvgd.hci.utah.edu; Tavtigian et al., 2006), and PhD-SNP (http://snps.biofold.org/phd-snp/phd-snp.html; Capriotti, Calabrese, & Cadadio, 2006).